Drag coefficient prediction for non-spherical particles in dense gas–solid two-phase flow using artificial neural network

Yan, Shengnan; He, Yurong; Tang, Tianqi; Wang, Tianyu

doi:10.1016/j.powtec.2019.05.049

Cited by 39 publications

(36 citation statements)

References 40 publications

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“…Then, the authors used the experimental data from the literature and the predicted drag coefficient data to make curve fitting of drag coefficient, sphericity, and Reynolds number. More details can be found in the authors' previous work (Yan et al 2019).…”

Section: Radial Basis Neural Networkmentioning

confidence: 99%

“…Gidaspow-Blend drag model (Huilin and Gidaspow 2003) Drag coefficient correlation based on artificial neural network (Yan et al 2019…”

Section: Cfd Modelmentioning

confidence: 99%

“…ANN has several outstanding advantages, one of which is the learning ability. In the previous paper (Yan et al 2019), it has been proved that ANN can well learn and master the complicated rule between the Reynolds number, particle sphericity, and drag coefficient. It is an efficient tool in predicting the drag coefficients of different kinds of shaped particles.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, TFM can better solve the problem of model scale, computation demand and cost, and efficiency (Rangarajan et al 2013;Langston et al 2009). In our previous paper (Yan et al 2019), a modified Gidaspow-Blend model was established based on the drag coefficient prediction by radial basis neural network (RBFNN). And it was validated in modeling hydrodynamics of non-spherical particles in a gas-solid fluidized bed.…”

Section: Introductionmentioning

confidence: 99%

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Simulation on hydrodynamics of non-spherical particulate system using a drag coefficient correlation based on artificial neural network

et al. 2019

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Fluidization of non-spherical particles is very common in petroleum engineering. Understanding the complex phenomenon of non-spherical particle flow is of great significance. In this paper, coupled with two-fluid model, the drag coefficient correlation based on artificial neural network was applied in the simulations of a bubbling fluidized bed filled with non-spherical particles. The simulation results were compared with the experimental data from the literature. Good agreement between the experimental data and the simulation results reveals that the modified drag model can accurately capture the interaction between the gas phase and solid phase. Then, several cases of different particles, including tetrahedron, cube, and sphere, together with the nylon beads used in the model validation, were employed in the simulations to study the effect of particle shape on the flow behaviors in the bubbling fluidized bed. Particle shape affects the hydrodynamics of non-spherical particles mainly on microscale. This work can be a basis and reference for the utilization of artificial neural network in the investigation of drag coefficient correlation in the dense gas-solid two-phase flow. Moreover, the proposed drag coefficient correlation provides one more option when investigating the hydrodynamics of non-spherical particles in the gas-solid fluidized bed.

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Section: Radial Basis Neural Networkmentioning

confidence: 99%

“…Gidaspow-Blend drag model (Huilin and Gidaspow 2003) Drag coefficient correlation based on artificial neural network (Yan et al 2019…”

Section: Cfd Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Simulation on hydrodynamics of non-spherical particulate system using a drag coefficient correlation based on artificial neural network

et al. 2019

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“…Then, these values can be used for the prediction of the output data corresponding to different input data. [14][15][16][17] Modeling with ANN has been used for the solution of many complex engineering problems such as nuclear engineering, 18 bioengineering, 19 thermal engineering, 20 metallurgical and materials engineering 21 or, automotive engineering, 22,23 and, in the analysis of many laminated or multilayer composites [24][25][26][27] until now. It is well known that corrosion is a dangerous and extremely costly problem in the widespread use of engineering materials.…”

Section: Introductionmentioning

confidence: 99%

An artificial neural network (ANN) solution to the prediction of age-hardening and corrosion behavior of an Al/TiC functional gradient material (FGM)

Dikici

Tuntaş

2020

Journal of Composite Materials

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In this theoretical study, the prediction of the corrosion resistance and age-hardening behavior of an Al/TiC functional gradient material (FGM) has been investigated by using the artificial neural network (ANN). The input parameters have been selected as TiC volume fraction of the composite layers, aging periods of the composite, environmental conditions, and applied potential during the corrosion tests. Current and microhardness were used as the one output in the proposed network. Also, a new three-layered composite has been imaginarily designed to demonstrate the predictive capability and flexibilities of the ANN model as a case study. Artificially aging (T6) process and potentiodynamic scanning (PDS) tests were used for heat-treating and corrosion response of the FGS, respectively. The results showed that the generated PDS curves of the FGM and calculated corrosion parameters of the case study are quite near and in acceptable limits for similar composites obtained values in experimental studies. Besides, this study has been a great success in predicting peak-aging times and its corresponding hardness values more precisely.

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CFD‐ANN coupling model simulation of gas–solid feeding design for ternary biomass mixtures in bubbling fluidized bed gasifier

Soanuch,

Pareek,

Piumsomboon

et al. 2024

Can J Chem Eng

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The performance of continuous feeding fluidized bed reactors is significantly influenced by their design. These reactors can effectively operate with a wide range of biomass mixtures. Therefore, it is imperative to carefully design the gas distributor plate and solid tube inlet to ensure stable fluidization and uniform distribution of fluidizing gas and solid particles within the reactor. This study investigated the impact of gas–solid feeder design in bubbling fluidized bed gasifier for biomass mixtures on system hydrodynamics, employing a computational fluid dynamics–artificial neural network (CFD‐ANN) coupling model to achieve more realistic simulations. A 2k factorial experimental design was adopted to inquire the impact of gas and solid feeding systems. The responses under investigation included the gas–solid mixing index and the solid residence time, both of which hold pivotal roles in specific chemical processes related to biomass utilization in fluidized bed technology. All cases were successfully simulated, and the results uncovered that the position and length of the solid inlet tube wielded a significant influence on reactor performance, particularly concerning solid residence time. Furthermore, the designs of the gas distributor were identified as critical factors capable of enhancing system turbulence and mixing. In summary, the results showed the potential for enhancing reactor performance through the optimization of gas–solid feeding systems and underscored the efficacy of the ANN drag model in simulating continuous biomass gasification systems.

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Drag coefficient prediction for non-spherical particles in dense gas–solid two-phase flow using artificial neural network

Cited by 39 publications

References 40 publications

Simulation on hydrodynamics of non-spherical particulate system using a drag coefficient correlation based on artificial neural network

Simulation on hydrodynamics of non-spherical particulate system using a drag coefficient correlation based on artificial neural network

An artificial neural network (ANN) solution to the prediction of age-hardening and corrosion behavior of an Al/TiC functional gradient material (FGM)

CFD‐ANN coupling model simulation of gas–solid feeding design for ternary biomass mixtures in bubbling fluidized bed gasifier

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